Acquiring and Assessing Knowledge from Multiple Experts Using Graphical Representations

The integrity of the process of eliciting and representing human expertise is fundamental to any knowledge-based system. This process is referred to as "knowledge acquisition." Human expertise can be found in documents and databases that store records of past transactions as well as the guidelines and procedures used to process those transactions. An active area of current research involves "mining" these data to capture knowledge to develop rules for incorporation into knowledge-based systems. This chapter presents a thorough review of current practice in eliciting, representing, and amalgamating knowledge from multiple experts with a focus on the use of graphical representations to support the process. The important points of the discussion are illustrated by presenting a specific methodology for eliciting and combining knowledge from multiple experts. This methodology provides a statistically defensive summarization for assessment. The results of a pilot test of its implementation over the Internet are also presented in the chapter. This chapter concludes with a discussion on the need for knowledge acquisition techniques that permit the quality of the rules developed based on the acquired knowledge to be quantitatively assessed and subsequently embedded into decision support systems.